



from ....configuration_utils import PretrainedConfig
from ....utils import logging


a      :  Any			     =			logging.get_logger(__name__)

a      :  Tuple			     =			{
    'Visual-Attention-Network/van-base': (
        'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
    ),
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''van'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=224,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=[7, 3, 3, 3],   SCREAMING_SNAKE_CASE_=[4, 2, 2, 2],   SCREAMING_SNAKE_CASE_=[64, 128, 320, 512],   SCREAMING_SNAKE_CASE_=[3, 3, 12, 3],   SCREAMING_SNAKE_CASE_=[8, 8, 4, 4],   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=1E-6,   SCREAMING_SNAKE_CASE_=1E-2,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   **SCREAMING_SNAKE_CASE_,   )       ->   Optional[int]:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     image_size
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		str							      =     patch_sizes
						UpperCAmelCase_:		Tuple							      =     strides
						UpperCAmelCase_:		Any							      =     hidden_sizes
						UpperCAmelCase_:		List[Any]							      =     depths
						UpperCAmelCase_:		Union[str, Any]							      =     mlp_ratios
						UpperCAmelCase_:		Union[str, Any]							      =     hidden_act
						UpperCAmelCase_:		int							      =     initializer_range
						UpperCAmelCase_:		List[Any]							      =     layer_norm_eps
						UpperCAmelCase_:		List[str]							      =     layer_scale_init_value
						UpperCAmelCase_:		Optional[Any]							      =     drop_path_rate
						UpperCAmelCase_:		List[str]							      =     dropout_rate











#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






